Entity Recognition Accuracy

Extended definition

Entity Recognition Accuracy measures whether AI systems correctly understand and represent your brand entities—company name, product names, key personnel, locations, and proprietary concepts. This goes beyond simple name matching to evaluate whether engines grasp relationships (this person works for this company), attributes (this product serves this use case), and distinctions (these are two separate offerings, not one). Accuracy is assessed through systematic testing of how engines describe your entities when asked directly and how they reference them in broader topic answers.

Why this matters for AI search visibility

Incorrect entity recognition leads to visibility loss, misattribution, and damaged credibility. If an AI engine confuses your company with a competitor, misattributes your CEO's quotes to someone else, or merges two distinct products into one, it distorts your brand narrative and sends prospects toward incorrect conclusions. High Entity Recognition Accuracy ensures that when you do appear in AI answers, the information is correct and reinforces rather than undermines your positioning.

Practical examples

• An AI engine consistently identifies 'Acme Analytics' as a separate entity from 'Acme Software,' maintaining proper product differentiation
• A CEO's thought leadership is correctly attributed 92% of the time across engines, but occasionally confused with a similarly-named industry peer
• A rebranded company sees Entity Recognition Accuracy drop from 88% to 34% as engines continue using the old name and outdated descriptions